The linkage between corruption and carbon dioxide emission evidence from asian countries

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE LINKAGE BETWEEN CORRUPTION AND CARBON DIOXIDE EMISSION: EVIDENCE FROM ASIAN COUNTRIES BY NGUYEN THAI DUONG MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, NOVEMBER 2016 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE LINKAGE BETWEEN CORRUPTION AND CARBON DIOXIDE EMISSION: EVIDENCE FROM ASIAN COUNTRIES A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN THAI DUONG Academic Supervisor: DR PHAM KHANH NAM HO CHI MINH CITY, NOVEMBER 2016 TABLE OF CONTENTS ACKNOWLEDGEMENT ABSTRACT .2 ABBREVIATIONS LIST OF FIGURES CHAPTER INTRODUCTION 1.1 Problem Statement 1.2 Research Objectives 1.3 Thesis Structure CHAPTER LITERATURE REVIEW 10 2.1 The corruption – growth relationship review 10 2.2 The growth – environment relationship review 13 2.3 The corruption – environment relationship review 16 CHAPTER METHODOLOGY 20 3.1 Analytical Framework 20 3.2 Model specification and estimation method 21 3.3 Data and variables 23 CHAPTER RESULT 29 4.1 Descriptive Statistic 29 4.2 Covariance matrix 32 4.3 Regression result 36 CHAPTER CONCLUSION .45 5.1 Conclusion 45 5.2 Policy Implications 46 5.3 Thesis limitations 46 5.4 Suggestion for further researches 47 REFERENCES 48 APPENDICES 55 ACKNOWLEDGEMENT Firstly, I would like to express my sincere gratitude to my advisor Dr Pham Khanh Nam for his continuous and solid support during my thesis writing process Several insightful comments based on his immense knowledge helped me to solve all my problems regarding to this thesis Besides my advisor, I would like to thank Dr Truong Dang Thuy for his useful advice on my methodology My special thanks also go to my colleagues who always create opportunities and arrange everything for me so that I could have adequate time to pursue my thesis Finally, I would like to send my love to my family and my close friends for always being beside me, spiritually encouraging me and letting me know that no matter what has happened I am not alone 1|Page ABSTRACT This research investigates the direct and indirect effects of corruption which measured by corruption perception index on carbon dioxide emissions Using data from 42 Asian countries and applying three-stage least squares (3SLS) method with considering corruption as endogenous variable, the finding indicates both effects are positive implying that countries should reduce their corruption levels to lower poison gas emission Although these effects are not clear when we control for fixed effects using countries dummies, these are significant when we use Asian subregions dummy instead In addition, we also find that capital per worker and human capital possess positive relationships with economic growth while the share of export and import in GDP positively affects carbon dioxide emission Keywords: Corruption, economic growth, environment, carbon dioxide, Asian countries, three-stage least squares, endogeneity 2|Page ABBREVIATIONS 2SLS Two-stage least squares 3SLS Three-stage least squares CO2 Carbon dioxide CPI Corruption Perception Index EDGAR Emissions Database for Global Atmospheric Research EKC Environmental Kuznets Curve GDP Gross domestic product GFK Gross Fixed Capital Formation RF Radiative forcing 3|Page LIST OF FIGURES Figure 1.1: Carbon dioxide levels since 400,000 years ago Figure 2.1: Environmental Kuznets Curve .14 Figure 3.1: Conceptual Framework 21 Figure 3.2: Major Greenhouse Gases from People's Activities 25 Figure 4.1: A combination of three scatter plots show the correlations between our main variables, namely corruption – carbon dioxide – emission, corruption – income per capita and income per capita – carbon dioxide emission .34 4|Page LIST OF TABLES Table 3.1: Name of sub-regions and countries in the sample 23 Table 4.1: Descriptive Statistic 29 Table 4.2: Skewness and kurtosis value before and after taking natural logarithms 31 Table 4.3: Covariance matrix 35 Table 4.4: Three-stage least squares regression (pooled regression) 37 Table 4.5: Three-stage least squares regression with fixed effects of sub-regions and time 38 Table 4.6: The impact of corruption on pollution .40 Table 4.7: Three-stage least squares regression with fixed effects of countries and time 41 Table 4.8: Results of all three above regressions .44 5|Page CHAPTER INTRODUCTION 1.1 Problem Statement Climate change is one of the most important issues facing the world today Many serious observable influences on the environment due to global climate change have been seen: continuous rise in temperatures, stronger and more intense hurricanes, more droughts and heat waves, loss of sea ice, accelerated rise in sea level, etc Climate change is mainly caused by the emission of heat-trapping gases or greenhouse gases There are many sorts of greenhouse gases such as water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydro fluorocarbons (HFCs), chlorofluorocarbons (CFCs), per fluorocarbons (PFCs) or sulfur hexafluoride (SF6), but carbon dioxide which has accumulated without being any less strong in the atmosphere places us at the highest risk of serious ecological problems This is attributed to two key reasons First, among heat-trapping gases, CO2 has the highest positive “Radiative Forcing” (RF)1 Although, CO2 molecule has less heat-trapping ability than other gases’ molecule, the amount of CO2 in the atmosphere is the most abundant and is emitted into the air with the highest speed owing to daily human activities Second, the time that CO2 existing before totally leaving from the atmosphere is much longer than most of other greenhouse gases While methane takes about 10 years to decay and nitrous oxide takes a century, CO2 takes approximately 50-200 years to leave from the atmosphere Facing with this severe problem, many worldwide conferences have taken place aiming to discuss how to diminish greenhouse gases release, especially carbon dioxide release Typically, Kyoto Protocol, which was adopted in Kyoto, Japan, on 11th December 1997, is a commitment of countries around the world to limit the greenhouse gases emission within the allowable levels After several rounds of “Radiative Forcing” (RF) which is defined as the difference in the energy of the incoming solar radiation absorbed by the Earth and the energy of outgoing radiation is the factor affecting the temperature of the Earth’s surface The surface could be warmer if the RF gets positive value and cooler if the RF gets the negative one 6|Page discussion and amendment (e.g Marrakesh, Morocco, in 2001; Doha, Qatar, in 2012), this protocol officially became effective on 16th February 2005 Figure 1.1: Carbon dioxide levels since 400,000 years ago (Credit: Vostok ice core data/J.R Petit et al.; NOAA Mauna Loa CO2 record.) Besides practical activities in the endeavor to reduce CO2 emission all over the world, many researches have been implemented to figure out the determinants of environment pollution in general and air pollution in particular One of these important factors attracting researchers’ attentions is corruption “Corruption involves behavior on the part of officials in the public sector, whether politicians or civil servants, in which they improperly and unlawfully enrich themselves, or those close to them, by the misuse of the power entrusted to them” (Transparency International, 2000) The previous literature suggests that corruption can affect environment not only directly but also indirectly On the one hand, the environmental laws enforcement might be less effective under corruption, which results in higher pollution (see Hafner, 1998; Lippe, 1999) On the other hand, corruption might indirectly affect pollution through income transmission channel There is evidence that corruption could have harmful effects on the economic growth (Mauro, 1995; Hall and Jones, 1999) Then, pollution might reduce at some 7|Page REFERENCES Abed, G T., & Davoodi, H R (2000) Corruption, structural reforms, and economic performance in the transition economies Agras, J., & Chapman, D (1999) A dynamic approach to the Environmental Kuznets Curve hypothesis Ecological Economics, 28(2), 267-277 Aidt, T., Dutta, J., & Sena, V (2008) Governance regimes, corruption and growth: Theory and evidence Journal of Comparative Economics, 36(2), 195-220 Azomahou, T., Laisney, F., & Van, P N (2006) Economic development and CO emissions: a nonparametric panel approach Journal of Public Economics, 90(6), 1347-1363 B.R Copeland, M.S Taylor, North–south trade and the environment, Quart, J Econom 109 (1994) 755–787 Baltagi, B (2008) Econometric analysis of panel data John Wiley & Sons Ben-David, D., & Kimhi, A (2000) Trade and the rate of convergence.NBER Working Paper, 7642 Blecker, R (1999) Taming global finance Economic Policy Institute Brunetti, A., Kisunko, G., & Weder, B (1998) Credibility of rules and economic growth: Evidence from a worldwide survey of the private sector The World Bank Economic Review, 12(3), 353-384 Change, I C (2014) Mitigation of Climate Change Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change Cambridge University Press, Cambridge, UK and New York, NY Cole, M A (2004) US environmental load displacement: examining consumption, regulations and the role of NAFTA Ecological economics,48(4), 439-450 Cole, M A (2007) Corruption, income and the environment: an empirical analysis Ecological Economics, 62(3), 637-647 Cole, M A., & Rayner, A J (2000) The Uruguay round and air pollution: estimating the composition, scale and technique effects of trade 48 | P a g e liberalization The Journal of International Trade & Economic Development,9(3), 339-354 Cole, M A., Elliott, R J., & Fredriksson, P G (2006) Endogenous pollution havens: Does FDI influence environmental regulations? The Scandinavian Journal of Economics, 108(1), 157-178 Cole, M A., Rayner, A J., & Bates, J M (1997) The environmental Kuznets curve: an empirical analysis Environment and development economics, 2(04), 401-416 Copeland, B R., & Taylor, M S (1994) North-South trade and the environment The quarterly journal of Economics, 755-787 Damania, R., Fredriksson, P G., & List, J A (2003) Trade liberalization, corruption, and environmental policy formation: theory and evidence Journal of environmental economics and management, 46(3), 490-512 De Bruyn, S M., van den Bergh, J C., & Opschoor, J B (1998) Economic growth and emissions: reconsidering the empirical basis of environmental Kuznets curves Ecological Economics, 25(2), 161-175 DeSA, U N (2013) World population prospects: the 2012 revision Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, New York Desai, U (1998) Ecological policy and politics in developing countries: Economic growth, democracy, and environment SUNY Press Dridi, M (2013) Corruption and economic growth: the transmission channels Edwards, S (1992) Trade orientation, distortions and growth in developing countries Journal of development economics, 39(1), 31-57 Edwards, S (1995) Trade policy, exchange rates, and growth In Reform, recovery, and growth: Latin America and the Middle East (pp 13-52) University of Chicago Press Edwards, S (1998) Openness, productivity and growth: what we really know? The economic journal, 108(447), 383-398 49 | P a g e Fredriksson, P G., & Svensson, J (2003) Political instability, corruption and policy formation: the case of environmental policy Journal of public economics, 87(7), 1383-1405 Fredriksson, P G., Vollebergh, H R., & Dijkgraaf, E (2004) Corruption and energy efficiency in OECD countries: theory and evidence Journal of Environmental Economics and management, 47(2), 207-231 Friedl, B., & Getzner, M (2003) Determinants of CO2 emissions in a small open economy Ecological economics, 45(1), 133-148 Galeotti, M., & Lanza, A (1999) Richer and cleaner? A study on carbon dioxide emissions in developing countries Energy Policy, 27(10), 565-573 Galeotti, M., Lanza, A., & Pauli, F (2006) Reassessing the environmental Kuznets curve for CO emissions: a robustness exercise Ecological economics, 57(1), 152-163 Greene, W H (2003) Econometric analysis Pearson Education India Grossman, G M., & Krueger, A B (1991) Environmental impacts of a North American free trade agreement (No w3914) National Bureau of Economic Research Grossman, G M., & Krueger, A B (1993) 0Environmental Impacts of a North American Free Trade Agreement1 Garber P.(éd.), The US-Mexico Free Trade Agreement, MIT Press, Cambridge, MA, 1655177 Grossman, G M., & Krueger, A B (1994) Economic growth and the environment (No w4634) National Bureau of Economic Research Gupta, S., Davoodi, H., & Alonso-Terme, R (2002) Does corruption affect income inequality and poverty? Economics of governance, 3(1), 23-45 Gyimah-Brempong, K (2002) Corruption, economic growth, and income inequality in Africa Economics of Governance, 3(3), 183-209 Hafner, O (1998) The role of corruption in the misappropriation of tropical forest resources and in tropical forest destruction 50 | P a g e Hall, R E., & Jones, C I (1999) Why some countries produce so much more output per worker than others? (No w6564) National bureau of economic research Heckelman, J C., & Powell, B (2010) Corruption and the institutional environment for growth Comparative Economic Studies, 52(3), 351-378 Heil, M T., & Selden, T M (2001) Carbon emissions and economic development: future trajectories based on historical experience Environment and Development Economics, 6(01), 63-83 Helleiner, G K (1996) Trade, trade policy, and industrialization Unpublished manuscript Jain, A K (2001) Corruption: A review Journal of economic surveys, 15(1), 71121 Jayme Jr, F G (2001) Notes on trade and growth Texto para Discussão, (166) Krueger, A O (1997) Trade policy and economic development: how we learn (No w5896) National Bureau of Economic Research Leff, N H (1964) Economic development through bureaucratic corruption.American behavioral scientist, 8(3), 8-14 Leite, C A., & Weidmann, J (1999) Does mother nature corrupt? Natural resources, corruption, and economic growth Natural Resources, Corruption, and Economic Growth (June 1999) IMF Working Paper, (99/85) Levine, R., & Renelt, D (1992) A sensitivity analysis of cross-country growth regressions The American economic review, 942-963 Levine, R., & Zervos, S J (1993) What we have learned about policy and growth from cross-country regressions? The American Economic Review,83(2), 426430 Lindmark, M (2002) An EKC-pattern in historical perspective: carbon dioxide emissions, technology, fuel prices and growth in Sweden 1870– 1997.Ecological economics, 42(1), 333-347 51 | P a g e Lippe, M (1999) Corruption and environment at the local level Trans' parency International Working Paper Retrieved March, 31, 2014 Lopez, R (1994) The environment as a factor of production: the effects of economic growth and trade liberalization Journal of Environmental Economics and management, 27(2), 163-184 Lopez, R., & Mitra, S (2000) Corruption, pollution, and the Kuznets environment curve Journal of Environmental Economics and Management,40(2), 137-150 Lui, F T (1985) An equilibrium queuing model of bribery Journal of political economy, 93(4), 760-781 M.A Cole, A.J Rayner, The Uruguay round and air pollution:estimating the composition, scale and technique effects of trade liberalization, J Internat Trade Econom Development (2000) 343–358 Mankiw, N G., Romer, D., & Weil, D N (1990) A contribution to the empirics of economic growth (No w3541) National Bureau of Economic Research Martı́nez-Zarzoso, I., & Bengochea-Morancho, A (2004) Pooled mean group estimation of an environmental Kuznets curve for CO Economics Letters,82(1), 121-126 Mauro, P (1995) Corruption and growth The quarterly journal of economics, 681712 Mauro, P (1997) The Effect of Corruption on Growth, Investment and Government Expenditure: A cross country Analysis Corruption and Global Economy McCombie, J S., & Thirlwall, A P (1999) Growth in an international context: a Post Keynesian view Deprez, J and John Harvey Foundations of International Economics: Post Keynesian Perspectives London, Routledge Méndez, F., & Sepúlveda, F (2006) Corruption, growth and political regimes: Cross country evidence European Journal of Political Economy, 22(1), 82-98 Méon, P G., & Sekkat, K (2005) Does corruption grease or sand the wheels of growth? Public choice, 122(1-2), 69-97 52 | P a g e Méon, P G., & Weill, L (2010) Is corruption an efficient grease? World development, 38(3), 244-259 Mo, P H (2001) Corruption and economic growth Journal of comparative economics, 29(1), 66-79 Murphy, K., Shleifer, A., & Vishny, R (1991) nThe Allocation of Talent: Implication for Growth oQuarterly Journal of Economics, 106(2), 503 Myrdal, G (1968) Corruption: its causes and effects Asian drama: An inquiry into the poverty of nations, 2, 953-961 Panayotou, T (1993) Empirical tests and policy analysis of environmental degradation at different stages of economic development (No 292778) International Labour Organization Pellegrini, L (2011) Causes of corruption: a survey of cross-country analyses and extended results In Corruption, development and the environment (pp 29-51) Springer Netherlands Pellegrini, L., & Gerlagh, R (2004) Corruption's effect on growth and its transmission channels Kyklos, 57(3), 429-456 Roca, J., Padilla, E., Farré, M., & Galletto, V (2001) Economic growth and atmospheric pollution in Spain: discussing the environmental Kuznets curve hypothesis Ecological Economics, 39(1), 85-99 Rose-Ackerman, S (1997) Corruption, inefficiency and economic growth.Nordic Journal of Political Economy, 24, 3-20 Sachs, J D., Warner, A., Åslund, A., & Fischer, S (1995) Economic reform and the process of global integration Brookings papers on economic activity, 1995(1), 1-118 Sengupta, R (1996) CO2 emission–income relationship: policy approach for climate control Pacific and Asian Journal of Energy, 7(2), 207-229 Shafik, N., & Bandyopadhyay, S (1992) Economic growth and environmental quality: time-series and cross-country evidence (Vol 904) World Bank Publications 53 | P a g e Svensson, J., & Fisman, R J (2000) Are Corruption and Taxation Really Harmful to Growth? Firm-Level Evidence Firm-Level Evidence (November 2000) World Bank Policy Research Working Paper, (2485) Tanzi, V (1998) Corruption around the world: Causes, consequences, scope, and cures Staff Papers, 45(4), 559-594 Tanzi, V., & Davoodi, H R (2000) Corruption, growth, and public finances Welsch, H (2004) Corruption, growth, and the environment: a cross-country analysis Environment and Development Economics, 9(05), 663-693 York, R., Rosa, E A., & Dietz, T (2003) STIRPAT, IPAT and ImPACT: analytic tools for unpacking the driving forces of environmental impacts.Ecological economics, 46(3), 351-365 Zellner, A., & Theil, H (1962) Three-stage least squares: simultaneous estimation of simultaneous equations Econometrica: Journal of the Econometric Society, 54-78 54 | P a g e APPENDICES Appendix 1: Description of variables in the model Equation Variables Definition Unit Expected Sign Y Income per capita USD KPW Capital stock per worker USD + HK Human capital (literacy rate) % + POP Population growth % +/- INF Inflation rate % - TRADE Share of trade (import and export) in GDP % +/- CORR Corruption Perception Index +/- Equation Variables Definition E Carbon dioxide emissions per capita CORR Corruption Perception Index Y Income per capita IND TRADE Unit Expected Sign Kiloton/year + USD + The share of industry in GDP % + Share of trade (import and export) in GDP % + 55 | P a g e Appendix 2: Three-stage least squares regression (pooled regression) reg3 (lnY=CORR lnKPW HK POP INF lnTRADE) (lnE=CORR lnY lnY2 lnY3 lnIND lnTRAD > E), endog(CORR) Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" chi2 P lnY lnE 434 434 6 300561 1.472408 0.9554 0.3194 9304.60 417.76 0.0000 0.0000 Coef Std Err z P>|z| [95% Conf Interval] lnY CORR lnKPW HK POP INF lnTRADE _cons 0053316 8763076 1.201435 5.850097 -.0420207 0626464 -1.443749 0035111 0340711 1106248 7589209 2691045 0431265 4653184 1.52 25.72 10.86 7.71 -0.16 1.45 -3.10 0.129 0.000 0.000 0.000 0.876 0.146 0.002 -.0015501 8095296 9846141 4.36264 -.5694558 -.02188 -2.355756 0122134 9430857 1.418255 7.337555 4854145 1471728 -.5317417 CORR lnY lnY2 lnY3 lnIND lnTRADE _cons 0742845 56.41616 -6.659515 2642381 1559754 7375865 -156.7224 0130048 17.68301 2.120616 0832384 2630717 1716607 48.3921 5.71 3.19 -3.14 3.17 0.59 4.30 -3.24 0.000 0.001 0.002 0.002 0.553 0.000 0.001 0487955 21.75809 -10.81584 1010938 -.3596356 4011378 -251.5692 0997735 91.07423 -2.503184 4273823 6715864 1.074035 -61.87564 lnE Endogenous variables: Exogenous variables: lnY lnE CORR lnKPW HK POP INF lnTRADE lnY2 lnY3 lnIND 56 | P a g e Appendix 3: Three-stage least squares regression with fixed effects of sub-regions and time reg3 (lnY=CORR lnKPW HK POP INF lnTRADE i.year i.coderegion) (lnE=CORR lnY ln > Y2 lnY3 lnIND lnTRADE i.year i.coderegion), endog(CORR) Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" chi2 P lnY lnE 434 434 24 24 2743271 1.194582 0.9628 0.5520 11347.85 861.57 0.0000 0.0000 Coef Std Err z P>|z| [95% Conf Interval] lnY CORR lnKPW HK POP INF lnTRADE 007165 9310673 1.022217 7.128342 -.6612538 0994749 0037784 0404212 1527078 7819047 2630992 0533897 1.90 23.03 6.69 9.12 -2.51 1.86 0.058 0.000 0.000 0.000 0.012 0.062 -.0002406 8518432 7229152 5.595837 -1.176919 -.005167 0145706 1.010291 1.321519 8.660847 -.1455889 2041168 year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -.0722011 -.0255418 -.0292624 0095821 -.0206782 0005989 0551973 -.146934 -.087777 -.0620033 -.1005496 -.1181006 0890147 0822949 0822542 0808666 081948 08533 0866515 0888082 0885581 090858 0900936 0911012 -0.81 -0.31 -0.36 0.12 -0.25 0.01 0.64 -1.65 -0.99 -0.68 -1.12 -1.30 0.417 0.756 0.722 0.906 0.801 0.994 0.524 0.098 0.322 0.495 0.264 0.195 -.2466667 -.1868368 -.1904777 -.1489135 -.1812934 -.1666448 -.1146365 -.3209949 -.2613478 -.2400817 -.2771299 -.2966557 1022646 1357532 1319529 1680777 1399369 1678427 2250311 0271268 0857937 116075 0760307 0604544 coderegion -.2375648 2519532 0366768 -.0657151 1693639 -.2074802 0797963 0730659 0880577 056337 0607725 0665482 -2.98 3.45 0.42 -1.17 2.79 -3.12 0.003 0.001 0.677 0.243 0.005 0.002 -.3939625 1087467 -.1359131 -.1761337 0502519 -.3379123 -.081167 3951596 2092668 0447035 2884759 -.0770481 _cons -1.7926 5560725 -3.22 0.001 -2.882482 -.7027178 57 | P a g e lnE CORR lnY lnY2 lnY3 lnIND lnTRADE 0395584 72.69231 -8.462023 3283747 -.2627133 9938143 0148501 9.959184 1.188538 0465745 2328315 2215248 2.66 7.30 -7.12 7.05 -1.13 4.49 0.008 0.000 0.000 0.000 0.259 0.000 0104528 53.17267 -10.79151 2370904 -.7190548 5596337 068664 92.21195 -6.132531 419659 1936281 1.427995 year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -.0847447 -.3122003 -.6221195 -.7650945 -1.277854 -1.654213 -1.967542 -1.753561 -2.031267 -2.251956 -2.236048 -2.067424 3672401 3367668 334861 3305086 3338075 3480213 3571512 3502926 3547176 3636291 3569756 3616462 -0.23 -0.93 -1.86 -2.31 -3.83 -4.75 -5.51 -5.01 -5.73 -6.19 -6.26 -5.72 0.818 0.354 0.063 0.021 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 -.8045221 -.9722511 -1.278435 -1.412879 -1.932105 -2.336323 -2.667546 -2.440122 -2.7265 -2.964656 -2.935707 -2.776237 6350326 3478505 034196 -.1173096 -.6236038 -.9721043 -1.267539 -1.067 -1.336033 -1.539256 -1.536389 -1.35861 coderegion -.7194341 -.805053 -.9120255 -2.473405 -1.467415 -1.358924 3790635 3092806 3298344 2418689 278156 2714875 -1.90 -2.60 -2.77 -10.23 -5.28 -5.01 0.058 0.009 0.006 0.000 0.000 0.000 -1.462385 -1.411232 -1.558489 -2.94746 -2.012591 -1.89103 0235168 -.1988742 -.2655619 -1.999351 -.9222394 -.8268187 _cons -199.3512 27.58048 -7.23 0.000 -253.408 -145.2945 Endogenous variables: lnY lnE CORR Exogenous variables: lnKPW HK POP INF lnTRADE 2002.year 2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year 2012.year 2013.year 2.coderegion 3.coderegion 4.coderegion 5.coderegion 6.coderegion 7.coderegion lnY2 lnY3 lnIND 58 | P a g e Appendix 4: Three-stage least squares regression with fixed effects of countries and time reg3 (lnY=CORR lnKPW HK POP INF lnTRADE i.year i.codecountry) (lnE=CORR lnY l > nY2 lnY3 lnIND lnTRADE i.year i.codecountry), endog(CORR) Three-stage least-squares regression Equation Obs Parms RMSE "R-sq" chi2 P lnY lnE 434 434 58 58 2289026 1305283 0.9741 0.9947 15632.03 51811.93 0.0000 0.0000 Coef Std Err z P>|z| [95% Conf Interval] lnY CORR lnKPW HK POP INF lnTRADE 0581489 7539155 -.57835 -2.282611 -.0617293 217596 0141893 0603273 6413462 1.185636 2520347 1294891 4.10 12.50 -0.90 -1.93 -0.24 1.68 0.000 0.000 0.367 0.054 0.807 0.093 0303383 6356762 -1.835365 -4.606414 -.5557082 -.0361981 0859595 8721548 6786655 0411917 4322495 47139 year 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 -.0520004 -.013172 0306233 1787129 2000866 1849489 3027569 2029753 2852394 3501261 4459263 4107264 0775849 0744466 077682 0777198 0815778 0903015 094042 0943042 0991424 1055276 1128235 1171734 -0.67 -0.18 0.39 2.30 2.45 2.05 3.22 2.15 2.88 3.32 3.95 3.51 0.503 0.860 0.693 0.021 0.014 0.041 0.001 0.031 0.004 0.001 0.000 0.000 -.204064 -.1590847 -.1216306 0263848 0401969 0079613 118438 0181425 0909238 1432958 2247962 1810707 1000633 1327408 1828771 331041 3599762 3619366 4870758 3878081 479555 5569564 6670564 6403822 codecountry 10 1.746906 1.538453 3.402389 4061023 2.204469 8538889 2.073364 4.36569 2.057744 4842314 4642896 5726122 1706629 513939 3035798 4958777 7498692 5055307 3.61 3.31 5.94 2.38 4.29 2.81 4.18 5.82 4.07 0.000 0.001 0.000 0.017 0.000 0.005 0.000 0.000 0.000 7978302 6284619 2.280089 0716092 1.197167 2588836 1.101462 2.895973 1.066922 2.695982 2.448444 4.524688 7405953 3.211771 1.448894 3.045267 5.835406 3.048566 59 | P a g e 11 12 13 14 15 16 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 1.320382 1.596524 1.190477 4.460835 2.408012 1.942017 1.030055 1.092249 1.364851 2.74779 1.598227 9799807 3.44461 9726899 1.370982 4.966911 2.161386 2.587353 5.53483 3.043158 1.827407 1.347276 1.351488 2.057805 2099165 2.682068 7391134 3.422264 9145409 1.155446 7232453 3458827 4232305 4110162 9259941 5614395 4752785 4493824 300559 4437516 541825 4668999 2363552 580926 234758 435857 7605639 4799706 4705682 9610394 6245139 4616231 4110336 4520175 4637761 2403247 5221108 5068224 6537213 4572021 4257104 2552277 3.82 3.77 2.90 4.82 4.29 4.09 2.29 3.63 3.08 5.07 3.42 4.15 5.93 4.14 3.15 6.53 4.50 5.50 5.76 4.87 3.96 3.28 2.99 4.44 0.87 5.14 1.46 5.24 2.00 2.71 2.83 0.000 0.000 0.004 0.000 0.000 0.000 0.022 0.000 0.002 0.000 0.001 0.000 0.000 0.000 0.002 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.003 0.000 0.382 0.000 0.145 0.000 0.045 0.007 0.005 642464 767007 3849 2.64592 1.307611 1.010488 1492817 5031638 4951136 1.685833 6831198 5167329 2.306016 5125727 5167182 3.476233 1.22066 1.665056 3.651227 1.819133 9226425 5416649 4655503 1.14882 -.2611113 1.658749 -.2542402 2.140994 0184412 3210695 2230081 1.998299 2.42604 1.996054 6.27575 3.508413 2.873545 1.910828 1.681334 2.234588 3.809748 2.513334 1.443228 4.583204 1.432807 2.225246 6.457589 3.102111 3.50965 7.418433 4.267182 2.732172 2.152887 2.237427 2.966789 6809444 3.705386 1.732467 4.703535 1.810641 1.989823 1.223482 _cons -4.213622 1.342675 -3.14 0.002 -6.845216 -1.582028 CORR lnY lnY2 lnY3 lnIND lnTRADE -.0121529 -.2127534 0695479 -.0035329 -.0001234 1225995 0112272 5.839079 7102804 0287814 1219716 131145 -1.08 -0.04 0.10 -0.12 -0.00 0.93 0.279 0.971 0.922 0.902 0.999 0.350 -.0341579 -11.65714 -1.322576 -.0599435 -.2391834 -.13444 0098521 11.23163 1.461672 0528777 2389366 379639 year 2002 2003 2004 2005 2006 2007 2008 2009 0653139 0058268 0057261 -.0052434 -.0247206 -.0022669 -.0692053 -.0466142 0532093 0501626 0546906 0630247 081528 1013487 1236887 1257049 1.23 0.12 0.10 -0.08 -0.30 -0.02 -0.56 -0.37 0.220 0.908 0.917 0.934 0.762 0.982 0.576 0.711 -.0389745 -.0924902 -.1014654 -.1287695 -.1845126 -.2009067 -.3116306 -.2929912 1696023 1041438 1129176 1182827 1350713 1963728 1732201 1997629 lnE 60 | P a g e 2010 2011 2012 2013 -.063708 -.0663484 -.0949502 -.0861958 1427697 1622441 174983 174838 -0.45 -0.41 -0.54 -0.49 0.655 0.683 0.587 0.622 -.3435315 -.3843409 -.4379105 -.428872 2161155 2516442 2480101 2564805 codecountry 10 11 12 13 14 15 16 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 3.873793 4.521708 5.589867 2.460722 2.572531 1.557453 4.766364 4.450894 3.579143 3.73983 3.902654 4.945828 4.647854 4.152609 5.653883 3.796286 1.52349 4.41914 4.551338 4.7504 1.809827 5.138763 3.475906 3.256952 6.08737 5.60503 5.376487 4.00011 4.96552 2.927953 4.302254 3.639005 4.217874 -2.85002 4.268034 5.689501 1.833872 5.091777 3.671778 3.541048 3550075 3061543 5213277 1584537 5325837 1398438 4242101 7745316 3579767 3498884 3406514 3842061 1.052104 4574363 3461732 1446655 2194192 3158878 4449627 2904945 1305997 6038852 2717339 3058823 1.190145 3824607 5221879 9620206 6093354 3733593 3922734 0965216 3489424 2108493 4708524 2651936 6349924 1792792 2027823 2976371 10.91 14.77 10.72 15.53 4.83 11.14 11.24 5.75 10.00 10.69 11.46 12.87 4.42 9.08 16.33 26.24 6.94 13.99 10.23 16.35 13.86 8.51 12.79 10.65 5.11 14.66 10.30 4.16 8.15 7.84 10.97 37.70 12.09 -13.52 9.06 21.45 2.89 28.40 18.11 11.90 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.004 0.000 0.000 0.000 3.177992 3.921656 4.568083 2.150159 1.528686 1.283364 3.934927 2.93284 2.877521 3.054062 3.234989 4.192798 2.585767 3.256051 4.975396 3.512747 1.093436 3.800011 3.679227 4.181042 1.553856 3.95517 2.943318 2.657434 3.754729 4.855421 4.353017 2.114584 3.771245 2.196182 3.533412 3.449826 3.53396 -3.263277 3.34518 5.169731 5893094 4.740396 3.274332 2.95769 4.569595 5.121759 6.61165 2.771286 3.616376 1.831542 5.5978 5.968948 4.280764 4.425599 4.570318 5.698858 6.709941 5.049168 6.33237 4.079825 1.953544 5.038269 5.423449 5.319759 2.065797 6.322357 4.008495 3.856471 8.420011 6.354639 6.399956 5.885636 6.159796 3.659723 5.071096 3.828184 4.901789 -2.436763 5.190887 6.209271 3.078434 5.443158 4.069224 4.124406 _cons 3.743505 15.48346 0.24 0.809 -26.60353 34.09054 61 | P a g e Endogenous variables: lnY lnE CORR Exogenous variables: lnKPW HK POP INF lnTRADE 2002.year 2003.year 2004.year 2005.year 2006.year 2007.year 2008.year 2009.year 2010.year 2011.year 2012.year 2013.year 2.codecountry 3.codecountry 4.codecountry 5.codecountry 6.codecountry 7.codecountry 8.codecountry 9.codecountry 10.codecountry 11.codecountry 12.codecountry 13.codecountry 14.codecountry 15.codecountry 16.codecountry 18.codecountry 19.codecountry 20.codecountry 21.codecountry 22.codecountry 23.codecountry 24.codecountry 25.codecountry 26.codecountry 27.codecountry 28.codecountry 29.codecountry 30.codecountry 31.codecountry 32.codecountry 33.codecountry 34.codecountry 35.codecountry 36.codecountry 37.codecountry 38.codecountry 39.codecountry 40.codecountry 41.codecountry 42.codecountry lnY2 lnY3 lnIND 62 | P a g e ... STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS THE LINKAGE BETWEEN CORRUPTION AND CARBON DIOXIDE EMISSION: EVIDENCE FROM ASIAN COUNTRIES A thesis... and corrupt countries all over the world Using data from 42 Asian countries, we examine the relationship between corruption expressed by corruption perception index (CPI) and carbon dioxide emission. .. the corruption – growth, the growth – environment and the corruption – environment relationships respectively 2.1 The corruption – growth relationship review The theoretical behind the linkage between